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Page 16 of 34 Wang et al. Energy Mater. 2026, 6, 600064

Suppression of ion migration
While the implementation of robust hydrophobic architectures effectively shields the perovskite absorber
from extrinsic moisture degradation, continuous solar irradiation introduces another formidable challenge.
Under prolonged illumination and operating electric fields, the perovskite lattice becomes highly susceptible
to intrinsic structural instability driven by ion migration . Within this light-induced degradation process,
[138]
volatile halide anions represent the primary mobile species. Because these migrating anions inevitably trigger
severe compositional segregation and corrosive interfacial side reactions, implementing targeted
anion-immobilizing additive engineering strategies represents the most fundamental approach to device
stabilization. Degani et al. [139] introduced the additive 4-methylphenethylammonium bromide (MPEABr),
which reacts with residual PbI and undergoes spontaneous ion exchange driven by the thermodynamic
2
gradient of different halide anions, inducing the in situ formation of mixed-halide 2D perovskite phases with
a composition gradient. This effectively constrains the migration of volatile halide anions and suppresses
detrimental phase segregation throughout the active layer. By immobilizing mobile ionic species through this
structural design, photovoltaic devices achieve exceptional operational robustness, exhibiting stable
performance during outdoor stability tracking exceeding 800 h. Expanding on this concept, Kim et al. [140]
developed specialized zwitterionic interfacial modifiers, specifically 3-(1-pyridinio)-1-propanesulfonate.
These molecules possess a high inherent dipole moment arising from their spatially separated pyridinium
and sulfonate charges. By generating a robust local electrostatic field at the interface, these zwitterions
establish powerful electrostatic interactions with mobile ions to effectively anchor halide anions and organic
cations to their respective charged moieties. This molecular anchoring mechanism substantially elevates the
activation energy barrier for ion displacement, thereby suppressing electric-field-driven ion migration and
enhancing overall structural stability under severe thermal and electrical bias stress. Furthermore, while
intrinsic anion immobilization remains the primary focus of these additive strategies, modern interface
engineering must simultaneously address the detrimental migration of extrinsic cations. To this end,
Kim et al. [141] designed a novel molecular hole transport material, 1,3-bis(5-(4-(bis(4-methoxyphenyl)
amino)phenyl)thieno[3,2-b]thiophen-2-yl)-5-octyl-4H-thieno[3,4-c]pyrrole-4,6(5H)-dione (coded HL38).
This molecule features multiple oxygen and sulfur atoms whose electron-rich functional groups serve as
efficient molecular traps, enabling the coordination and capture of lithium ions derived from conventional
hygroscopic dopants. By establishing strong chemical interactions with these extrinsic cations, the HL38
layer prevents the diffusion of lithium ions across the perovskite absorber and suppresses their accumulation
at the opposite charge extraction boundaries. This targeted molecular immobilization enabled the
unencapsulated photovoltaic devices to retain ~86% of their initial efficiency even after 1,000 h of thermal
aging at 85 °C.

In summary, interfacial engineering plays a pivotal role in achieving long-term operational stability in PSCs.
The representative additive engineering strategies summarized in Table 2 are synergistic in nature, rather
than mutually exclusive. Looking ahead, the integration of multifunctional molecular designs and composite
interfacial architectures offers a promising route toward synergistic stabilization by unifying chemical
anchoring, physical shielding, hydrophobic protection, and ion regulation within a single framework.
Furthermore, to accelerate the rational design of such advanced interfaces, ML is emerging as a
transformative tool. By training on large datasets of molecular structures, interfacial properties, and device
performance metrics, ML models can predict optimal additive candidates, identify structure-property
relationships, and guide high-throughput experimental validation. This convergence of additive engineering
and data-driven intelligence enables a shift from trial-and-error screening to predictive materials discovery,
significantly shortening development cycles. Ultimately, the integration of interfacial stabilization with
optimal energy-level alignment, scalable fabrication, and robust encapsulation, guided by machine learning,
will be crucial to realizing reliable and high-performance perovskite photovoltaics for commercial
deployment.